MIND-Calib: Multi-View, Intensity and Depth-Driven Dense 2D–3D Alignment for Single-Frame LiDAR–Camera Extrinsic Calibration

Abstract

Extrinsic calibration between LiDAR and camera is a crucial step in multi-sensor fusion, where targetless approaches have attracted increasing attention for their flexibility and reusability. However, existing methods still suffer from three major limitations: time-consuming data preparation, lack of robustness under sparse single-frame input, and limited generalization across diverse LiDAR architectures. We propose MIND-Calib, a truly single-frame, targetless calibration framework. The method generates depth and intensity images through virtual multi-view projection, and performs image-domain completion and back-projection to densify the point cloud and construct sub-pixel 2D--3D correspondences. High-precision extrinsics are then estimated via dual-channel cross-modal matching that leverages both depth and intensity modalities. Experiments on three representative LiDAR types (MEMS-based, solid-state, and mechanical spinning) as well as on public datasets demonstrate an average accuracy of 2.85 cm (with respect to an average scene depth of 40 meters) in translation and 0.20°in rotation. More importantly, MIND-Calib not only achieves true single-frame calibration without any additional preparation, but also maintains stable accuracy under sparse inputs and exhibits strong generalization and robustness across devices and challenging environments.